added atrous_conv2d

hycis · hycis · commit 3fb2d343c0e2 · 2018-04-13T15:29:46.000+08:00
diff --git a/.travis.yml b/.travis.yml
@@ -3,6 +3,7 @@ language: python
 python:
   - "2.7"
   - "3.5"
+cache: pip
 install:
 
   - if [[ "$TRAVIS_PYTHON_VERSION" == "2.7" ]]; then
diff --git a/tensorgraph/layers/conv.py b/tensorgraph/layers/conv.py
@@ -123,6 +123,50 @@ def _variables(self):
         return [self.filter, self.b]
 
 
+class Atrous_Conv2D(Template):
+
+    @Template.init_name_scope
+    def __init__(self, input_channels, num_filters, rate=1, kernel_size=(3,3),
+                 filter=None, b=None, padding='VALID', stddev=0.1):
+        '''
+        PARAM:
+            padding: "SAME", same as input shape
+                     "VALID", 0 padding, output shape = h - 2*((k+(k-1)*(r-1))/2)
+                              where k: kernel_size, r: rate and effective filter
+                              size is k+(k-1)*(r-1) with (r-1) zeros inserted
+                              between every horizontal and vertical neighbouring
+                              filter values.
+        '''
+        self.input_channels = input_channels
+        self.num_filters = num_filters
+        self.kernel_size = kernel_size
+        self.rate = rate
+        self.padding = padding
+
+        self.filter_shape = self.kernel_size + (self.input_channels, self.num_filters)
+        self.filter = filter
+        if self.filter is None:
+            self.filter = tf.Variable(tf.random_normal(self.filter_shape, stddev=stddev),
+                                      name=self.__class__.__name__ + '_filter')
+
+        self.b = b
+        if self.b is None:
+            self.b = tf.Variable(tf.zeros([self.num_filters]), name=self.__class__.__name__ + '_b')
+
+
+    def _train_fprop(self, state_below):
+        '''state_below: (b, h, w, c)
+        '''
+        conv_out = tf.nn.atrous_conv2d(state_below, self.filter, rate=self.rate, padding=self.padding)
+        return tf.nn.bias_add(conv_out, self.b)
+
+
+    @property
+    def _variables(self):
+        return [self.filter, self.b]
+
+
+
 class Depthwise_Conv2D(Template):
 
     @Template.init_name_scope
diff --git a/test/layer_conv_test.py b/test/layer_conv_test.py
@@ -1,7 +1,7 @@
 
 import tensorflow as tf
 import tensorgraph as tg
-from tensorgraph.layers import Depthwise_Conv2D
+from tensorgraph.layers import Depthwise_Conv2D, Atrous_Conv2D
 import numpy as np
 
 def test_Depthwise_Conv2d():
@@ -19,5 +19,41 @@ def test_Depthwise_Conv2d():
         print(out.shape)
 
 
+def test_Atrous_Conv2d():
+
+    seq = tg.Sequential()
+    seq.add(Atrous_Conv2D(input_channels=5, num_filters=2, kernel_size=(3, 3), rate=3, padding='SAME'))
+
+    h, w, c = 100, 300, 5
+    X_ph = tf.placeholder('float32', [None, h, w, c])
+
+    y_sb = seq.train_fprop(X_ph)
+    with tf.Session() as  sess:
+        init = tf.global_variables_initializer()
+        sess.run(init)
+        out = sess.run(y_sb, feed_dict={X_ph:np.random.rand(32, h, w, c)})
+        print(out.shape)
+        assert out.shape[1] == h and out.shape[2] == w
+
+
+    seq = tg.Sequential()
+    r = 2
+    k = 5
+    seq.add(Atrous_Conv2D(input_channels=5, num_filters=2, kernel_size=(k, k), rate=r, padding='VALID'))
+
+    h, w, c = 100, 300, 5
+    X_ph = tf.placeholder('float32', [None, h, w, c])
+
+    y_sb = seq.train_fprop(X_ph)
+    with tf.Session() as  sess:
+        init = tf.global_variables_initializer()
+        sess.run(init)
+        out = sess.run(y_sb, feed_dict={X_ph:np.random.rand(32, h, w, c)})
+        print(out.shape)
+        assert out.shape[1] == h - 2*((k+(k-1)*(r-1))/2), out.shape[2] == w - 2*((w+(w-1)*(r-1))/2)
+
+
+
 if __name__ == '__main__':
-    test_Depthwise_Conv2d()
+    # test_Depthwise_Conv2d()
+    test_Atrous_Conv2d()
